At present, big data has become a technical hot spot in the current industry. And particularly, with the deployment of cloud computing services, great expectations are placed on the big data, which serves as the most important business application of the cloud computing services.
The big data is mainly characterized in that a volume of the big data is large, there is a multiple variety of the big data, speed of the big data is high and the value of the big data is high. Data characteristics of multi-source, heterogeneous and magnanimity are accompanied at the same time.
Data types involved in the big data include a structured type, a semi-structured type and an un-structured type and the like, so that a big data exchange system needs to distinguish and process these different data types. For example, collection, abnormal data cleaning, Extraction Transform Load (ETL) of structured database data, and incremental real-time collection, analysis, annotation, abnormal data cleaning, data segmentation, feature extraction, storage and caching, batch non-real-time processing of semi-structured data and non-structured data and the like.
Data objects involved in the big data include words, voices, videos, images, web pages, sensor data and the like, so that the big data exchange system needs to specifically distinguish and process data bearing formats corresponding to different data objects. The data bearing formats include, but not limited to, a text file, an audio file, an adaptation file, an image file and a webpage file, a sensor data file and corresponding real-time or quasi-real-time streaming media data.
In the related art, big data are gradually applied to all industries. However, as a volume of data is gradually increased, types of the data are increasingly complex, a generated speed of the data is rapidly increased, and a demand for a potential value utilization of the data is enhanced, the big data is subject to some new difficulties and faces new challenges which include the following points.
One, big data cannot be opened, shared and interconnected due to an islanding of the big data, so that a possibility that big data users share data of different industries and even different departments of the same industry is reduced.
Two, a large amount of the big data, lack of value measurement means and monetization means, having a huge potential value weakens enthusiasm of data owners to open data.
Three, lack of existing public data hinders public social resources from contribution of possible social life and economic activities.
Four, lack of an effective business mode, especially an efficient data exchange mechanism prevents safe and effective data exchange between data owners and data users.
Five, lack of interconnection and interworking standards cannot guarantee interconnection between big data providers and big data service providers.
For fully mining huge potential value from a large amount, various types and high-speed changes of big data, various problems and related challenges mentioned above are needed to be solved. However, in the related art, no effective solution is provided for problems of incapability of opening, interconnection and sharing of big data.